Search Results (484)

Polypropylene fibers provide an innovative solution for enhancing the crack resistance of tunnel lining segments. However, existing macro-models obscure the distinct effects of fibers on the mortar and ITZ, while explicit meso-modeling remains computationally prohibitive. This study develops a multi-scale modeling framework to investigate PFRC segment fracture under bending. The framework integrates a 3D meso-scale module for calibrating fracture-related material properties, a 3D macro-scale module for predicting global displacements, and a 2D meso-scale module for resolving local fracture processes. A full-scale bending test was performed to validate the framework and to examine the effects of fiber content at both scales. Both the full-scale test and numerical simulations show that the segment response exhibits three stages: elastic, damage development, and cracking at the design load. Numerical simulations further reveal that an optimal fiber content of 0.4% reduces the vertical displacement at the load point by 9.8% and the horizontal displacement at the edge point by 2.9% relative to the fiber-free case. Meso-scale simulations show that 0.4% fibers decrease the bottom crack width from 0.0868 to 0.0770 mm (−11.29%) and limit internal crack connectivity. Although fibers may locally promote ITZ cracking due to reduced mortar–aggregate bonding, a strengthened mortar matrix suppresses crack penetration and connected crack networks. A pronounced high-damage peak in the ITZ near the failure threshold confirms the ITZ as the governing weak link; therefore, further improvements may require ITZ-strengthening strategies. Full article

Prediction of fatigue failure in concrete structures remains a major challenge due to progressive material degradation. Reliable prediction, therefore, requires modeling the 3D heterogeneous microstructure of concrete to explain the underlying mechanisms governing fatigue failure. While such mesoscale models can reliably predict the fatigue-induced fracture mechanisms, the identification of the associated material parameters remains a significant challenge due to the high-dimensional parameter space introduced by the model. The key challenge addressed in this study is to capture microcrack initiation and coalescence under fatigue loading, using a model capable of representing fracture process: crack initiation, crack propagation, and final failure. Firstly, concrete domain is discretized into Voronoi cells, enabling explicit representation of aggregates and mortar by randomly assigning cohesive links connecting Voronoi cells as aggregates and mortar. After this, mortar links are modeled as coupled damage–plasticity 3D Timoshenko beam elements with nonlinear kinematic hardening and isotropic softening introduced using embedded discontinuity formulation, enabling fracture Modes I–III, whereas aggregate links are modeled as elastic 3D Timoshenko beam elements. The model efficiency is additionally reinforced by using surrogate model approach, with corresponding material parameter identification carried out by multi-objective Bayesian optimization framework to reproduce experimental results. The performance of the proposed model is illustrated by reproducing experimental results obtained from concrete cube compression test and three-point bending test under low-cycle fatigue loading, where the errors between experimental and numerical results are reduced by 82% (stress) and 88% (energy) for the cube test and by 86% (force) and 93% (energy) for the bending test, relative to the initial dataset error. Full article

The exceptional mechanical strength and toughness of collagen arise from its well-defined hierarchical architecture. Conventional methods for obtaining collagen aggregates (CAs), such as direct extraction from native tissues or acid swelling followed by mechanical processing, offer limited control over dimensional uniformity and provide little insight into the underlying exfoliation mechanisms. To overcome these challenges, this study introduces a novel strategy that leverages insights into the hierarchical interactions within collagen. We employ the ionic liquid 1-allyl-3-methylimidazolium chloride ([AMIM]Cl) as an exfoliating agent to successfully isolate fibrous CAs from native bovine tendon. By precisely modulating temperature and processing time, we achieve CAs with tunable mesoscale dimensions (diameter 0.9–1.1 μm, length > 160 μm). Molecular dynamics simulations reveal that [AMIM]Cl disrupts the intramolecular hydrogen-bonding network within collagen, thereby facilitating controlled exfoliation. These exfoliated aggregates serve as fundamental building blocks for fabricating collagen films. The resulting materials exhibit robust mechanical integrity, high transparency, reversible pH-responsive behavior, and excellent biocompatibility as verified by cytotoxicity assays, which together underscore their potential as versatile biomaterial platforms. Furthermore, the integration of single-walled carbon nanotubes yields conductive composites with confirmed electrical functionality. This study thus presents an innovative pathway for the precision processing of collagen and advances the design of high-performance collagen-based biomaterials. Full article

Assessing urban resilience under compound shocks requires observable and comparable process evidence that can inform resilient land governance and cross-jurisdiction planning. Using China’s Pilot Free Trade Zones (PFTZs) as a staged institutional setting, this research examines whether institutional exposure is associated with deviation–recovery trajectories of urban activity during the 2020 COVID-19 shock and whether these associations propagate through spatial spillovers with an identifiable scale profile. Institutional exposure is operationalized by the prefecture-level cities actually covered by PFTZ functional areas. With harmonized administrative boundaries, we construct an annual city-level VIIRS nighttime light (NTL) series for 2013–2024 and treat NTL as an activity-change signal rather than a direct proxy for output. We trace shock deviation in 2020 and subsequent recovery via staged differencing. Spatial interaction frictions are represented by least-cost path distance (LCPD) derived from a multi-source cost surface, which is used to build a gravity-based spatial weight matrix. Estimation relies on the Spatial Durbin Model (SDM), with LeSage–Pace impact decomposition to distinguish direct and spillover effects, complemented by distance-threshold diagnostics to map attenuation patterns. Results indicate persistent clustering within the PFTZ-related urban system. The shock year is characterized by compressed connectivity and fragmented brightening, whereas recovery proceeds in a layered manner with earlier core repair, partial corridor reconnection, and weaker adjustment at the periphery. Spatial dependence in activity change is statistically significant. Associations linked to institutional exposure are realized primarily locally, while structural and scale conditions more readily operate through spatial externalities. Spillovers are most detectable at meso-scales and attenuate gradually across distance thresholds. Overall, the integrated earth-observation and spatial-econometric framework provides replicable geospatial evidence to support resilient land governance and regional coordination under common shocks. Full article

This study investigates the scaling properties of rainfall in Tunis over temporal scales ranging from 5 min to 2.5 years using high-resolution rain gauge data from three recording stations. We employ the Universal Multifractal (UM) framework to characterize scaling properties across multiple temporal regimes. The UM model was selected over alternative multifractal approaches because of its parsimonious three-parameter formulation (C₁, α, H). It explicitly accounts for non-conservative processes through the Fractionally Integrated Flux (FIF) extension and includes established bias correction methods for highly intermittent signals. This framework has demonstrated universality across diverse climatic conditions and enables direct comparison with existing rainfall studies in Mediterranean environments. Spectral analysis reveals three distinct scaling regimes: micro-scale (5 min–2 h 40 min), meso-scale (2 h 40 min–7 days), and synoptic scale (>7 days). The non-conservative nature of the micro-scale regime is addressed through a multifractal fractionally integrated flux model. A key challenge in applying UM analysis to rainfall data is the prevalence of low and zero rain rates (>98% zeros in our dataset). This extreme intermittency introduces significant bias in parameter estimation. Existing correction methods require either continuous rain sequences—scarce in semi-arid climates—or are limited to moderate intermittency levels. We propose an empirical correction method that extends the existing semi-empirical approach by explicitly linking the percentage of zero values to biased UM parameters through empirical relationships applicable to sequences with as few as 50% rainy observations. This advancement enables reliable parameter estimation from highly intermittent datasets. In such conditions, traditional event-by-event analysis yields insufficient samples (only five continuous events longer than 2 h 40 min over 2.5 years in Tunis). The corrected estimates (α = 1.63, C₁ = 0.16 for micro-scales) demonstrate strong consistency with continuous rainfall events and align well with high-resolution studies, validating our approach for extreme intermittency conditions characteristic of Mediterranean semi-arid climates. Full article

Low-level jets (LLJs) are important mesoscale features in the Arctic and are highly relevant for the atmospheric transport of heat, moisture, and air pollutants, as well as for wind energy and aircraft operations. In this paper, LLJs at the Tiksi observatory in the Laptev Sea region are investigated during the period 2014–2020 using simulations performed with the regional climate model CCLM with a 5 km resolution. The main synoptic weather patterns for LLJs at Tiksi were identified using a self-organizing map (SOM) analysis. LLJs occurred in about 55% of all profiles with an average height of about 400 m and an average speed of about 13 m/s. About 60% of the LLJs had core speeds larger than 10 m/s (strong jets). The occurrence frequency for all jets showed a pronounced seasonal cycle with more and stronger LLJs during winter. The turbulent kinetic energy in the lower ABL was four times as large for LLJs than for situations without LLJs, which underlines the impact of LLJs on turbulent processes in the ABL. The mean duration of LLJ events (duration of at least 6 h) was almost 24 h and the 90th percentile was about two days. About 70% of the LLJ events were associated with downslope winds of the local mountain ridge and had a longer duration of about three days for the 90th percentile. Full article

Nucleation remains one of the least understood steps during protein crystallization, although it strongly impacts product quality attributes, including total crystal numbers, final crystal size distributions, and thus downstream processing. In this work, the nucleation behavior of Lactobacillus brevis alcohol dehydrogenase (LbADH) wild type (WT) and five mutants (Q207D, Q126H, K32A, D54F, and T102E) is investigated in a stirred 7 mL crystallizer monitored by in situ multi-angle dynamic light scattering (MADLS). Nucleation was studied with highly pure homotetrameric LbADHs by establishing a crystallization, lyophilization, and re-solubilization protocol combined with size exclusion chromatography (SEC) and size exclusion high-performance liquid chromatography (SE-HPLC), yielding tetramer purities above 94% and removing low molecular weight impurities. During stirred batch crystallizations initiated by the addition of polyethyleneglycol 550 monomethyl ether (PEG 550 MME), SEC and SE-HPLC revealed decreasing tetramer peak areas but essentially constant peak apex positions, indicating that no long-lasting oligomeric intermediates accumulate at detectable levels. Time-resolved MADLS measurements using a custom-made flow-through cuvette in a bypass to the stirred crystallizer uncovered transient cluster populations. All protein variants exhibited an initial tetramer peak, followed by the formation of larger aggregates and a rapid rise in signal above a hydrodynamic diameter of 1000 nm, coinciding with the onset of macroscopic turbidity. A simple mesoscale nucleation model was formulated, yielding end-of-nucleation times, crystallized fractions, critical soluble concentrations, and apparent nucleation rate constants. The crystal contact mutations modulate both the timing and magnitude of the nucleation burst (rapid build-up of nuclei/cluster populations). The mutant Q207D showed strongly attenuated nucleation compared to the WT, whereas the other mutants (K32A, D54F, and particularly T102E) display markedly accelerated nucleation at nearly invariant critical concentrations. The combined workflow demonstrates how in situ MADLS, together with a tailored kinetic description, can provide mechanistic insight into protein nucleation in stirred batch crystallizers. Full article

Failure of concrete structures is a multiscale process where macroscale at the structural level and mesoscale at the heterogeneous material level are both involved. A multiscale approach is necessitated in the simulation of concrete failure. Based on an overlapping domain decomposition method, a concurrent multiscale framework for the damage analysis of concrete structures is formulated. The applicability of the proposed framework is illustrated by the multiscale damage analysis of an L-shaped concrete structure. Considering the complexity of a mesoscale model for a global concrete structure, the concrete structure is divided into three parts that require different strategies. Special attention is paid to the part where mesoscale structure needs to be taken. The Concrete Damaged Plasticity (CDP) model is adopted at the mesoscale level. The numerical results indicate that the proposed framework is able to model the damage process in concrete structure where a critical area will be particularly considered. The computational efficiency of the concurrent nonlinear algorithm is also discussed. The proposed multiscale framework can be potentially applied to model structural damage analysis in engineering practice. Full article

Cyclonic extreme events have recently undergone an important boost over the Mediterranean Sea, a trend closely linked to ongoing strong climate variations. Several studies are explaining the combination of many different effects that increase the frequency of mesoscale vortices’ intensification, namely Mediterranean tropical-like cyclones (TLCs), until the stage of Medicanes. Among these effects, processes like sea–atmosphere energy exchanges, baroclinic instability, and the release of latent heat lead to the intensification of these systems into fully tropical-like structures. This study investigates the formation and development of Ianos, the most intense Mediterranean tropical-like cyclone recorded in recent years, which affected the Ionian Sea and surrounding regions in September 2020. Using satellite observations and remote sensing data, the study applies a dual approach to characterise the system evolution across the spatial and temporal scales. Firstly, proper orthogonal decomposition (POD) is exploited to assess temperature and pressure fluctuations derived from the geostationary database of Meteosat Second Generation (MSG-11)/SEVIRI. POD allows for the identification of dominant modes of variability and the quantification of energy distribution across different spatial structures during the cyclone’s lifecycle. The decomposition reveals that a small number of orthogonal modes capture a significant proportion of the total variance, highlighting the emergence and persistence of coherent structures associated with the cyclone’s core and peripheral convection. To support scale-dependent energy organisation and dissipation within Ianos, total-period and three-period analyses were carried out, in addition to early-stage intensification patterns and implications for meteorological scale assessments. From the study on the temperatures’ spatio-temporal evolution, a comparison in the POD spectra and of the structures during the peak of intensity was carried out between the Ianos TLC and the Faraji and Freddy tropical cyclones. Additional multi-sensor data from Suomi NPP and Sentinel-3 satellites were integrated to analyse the evolution of the same parameters, also taking into account an evaluation of the vertical temperature gradient, over a 4-day period encompassing the full life cycle of Ianos. The study of the daily evolution helps investigate the spatial trends around the warm core regions, identifying the pressure minima for a comparison with the BOLAM and ERA5 databases of the mean sea level pressure. Overall, this study demonstrates the value of combining dynamic decomposition methods with high-resolution satellite datasets to gain insight into the multiscale structure and convective energetics of Mediterranean tropical-like cyclones. Some significant patterns come out from the spatial organisation of deep convection that seem to be linked to the permanent structures of atmospheric fluctuations near the warm core centre. Full article

The traditional analysis of the mechanical performance of steel fiber-reinforced concrete (SFRC) predominantly relies on the assumption of an ideally random fiber distribution. This approach fails to account for the significant distribution inhomogeneity caused by practical construction processes like vibration, creating a discrepancy between simulation and reality. To address this, the main aim of this study was to demonstrate the critical impact of realistic fiber distribution on mechanical behavior by integrating image recognition with meso-mechanical simulation. Multi-factor controlled experiments were conducted to investigate the influence of vibration energy, fiber content, and aggregate volume fraction. An image recognition method was developed to accurately characterize the real spatial distribution of fibers, and these data were used to construct a three-dimensional meso-scale finite element model. Compared with the traditional model assuming random distribution, the proposed model based on the actual distribution showed significantly improved agreement with experimental results in terms of crack propagation paths and reduced the prediction error of the initial cracking load by more than 16.3%. For practitioners, the key takeaway is that modeling based on the actual fiber distribution is crucial for achieving realistic simulations. Our work provides a validated methodology to incorporate real distribution data, thereby improving the reliability of numerical assessments for SFRC structures, rather than relying on idealized random distribution assumptions. Full article

This study introduces a mesoscale modeling methodology for polyurethane-solidified ballast beds (PSBBs) that eliminates reliance on X-ray computed tomography (XCT) and addresses constraints in specimen size, capital cost, and post-processing complexity. The approach couples the Discrete Element Method (DEM) with the Finite Element Method (FEM). A high-fidelity discrete-element geometry is reconstructed from three-dimensional laser scans of ballast particles. The virtual-ray casting algorithm is then employed to identify the spatial distribution of ballast and polyurethane and map this information onto the finite-element mesh, enabling heterogeneous material reconstruction at the mesoscale. The accuracy of the model and mesh convergence are validated through comparisons with laboratory uniaxial compression tests, determining the optimal mesh size to be 0.4 times the minimum particle size (0.4 D_min). Based on this, a parametric study on the effect of sleeper width on ballast bed mechanical responses is conducted, revealing that when the sleeper width is no less than 0.73 times the ballast bed width (0.73 W_b) an optimal balance between stress diffusion and displacement control is achieved. This method demonstrates excellent cross-material applicability and can be extended to mesoscale modeling and performance evaluation of other multiphase particle–binder composite systems. Full article

Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to ensure quality, reliability, and scalability. This review provides a critical synthesis of advances in physics-based simulations, machine learning, and digital twin frameworks for PBF. We analyze progress across scales—from micro-scale melt pool dynamics and mesoscale track stability to part-scale residual stress predictions—while highlighting the growing role of hybrid physics–data-driven approaches in capturing process–structure–property (PSP) relationships. Special emphasis is given to the integration of real-time sensing, multi-scale modeling, and AI-enhanced optimization, which together form the foundation of emerging PBF digital twins. Key challenges—including computational cost, data scarcity, and model interoperability—are critically examined, alongside opportunities for scalable, interpretable, and industry-ready digital twin platforms. By outlining both the current state-of-the-art and future research priorities, this review positions digital twins as a transformative paradigm for advancing PBF toward reliable, high-quality, and industrially scalable manufacturing. Full article

The Weather Research and Forecasting Model (WRF-ARW) version 4.5 was used to simulate the synoptic to mesoscale evolving atmosphere of Tropical Cyclone (TC) Hilary’s (2023) remnant passage over the southwestern United States. The atmospheric dynamic processes conducive to the precursor rain events were extensively studied to determine the effects of mid-level jetogenesis. Concurrently, the dynamics of mesoscale processes related to the interaction of TC Hilary over the complex topography of the western United States were studied with several sensitivity simulations on a nested 2 km × 2 km grid. The differential surface heating between the cloudy California coast and clear/elevated Great Basin plateau had a profound impact on the lower-mid-tropospheric mass field resulting in mid-level jetogenesis. Diagnostic analyses of the ageostrophic flow support the importance of both isallobaric and inertial advective forcing of the mid-level jetogenesis in response to differential surface sensible heating. This ageostrophic mesoscale jet ultimately transported tropical moisture in multiple plumes more than 1000 km poleward beyond the location of the extratropical transition of the storm, resulting in anomalous flooding precipitation within a massive arid western plateau. Full article

Hydrogen-based direct reduction (H-DR) represents an environmentally benign and energy-efficient alternative in ironmaking that has significant industrial potential. This study reviews the current status of H-DR shaft furnaces and accompanying hydrogen-rich reforming technologies (steam and autothermal reforming), assessing the three dominant numerical frameworks used to analyze these processes: (i) porous medium continuum models, (ii) the Eulerian two-fluid model (TFMs), and (iii) coupled computational fluid dynamics (CFD)–discrete element method (DEM) models. The respective trade-offs in terms of computational cost and model accuracy are critically compared. Recent progress is evaluated from an engineering standpoint in four key areas: optimization of the pellet bed structure and gas distribution, thermal control of the reduction zone, sensitivity analysis of operating parameters, and industrial-scale model validation. Current limitations in predictive accuracy, computational efficiency, and plant-level transferability are identified, and possible mitigation strategies are discussed. Looking forward, high-fidelity multi-physics coupling, advanced mesoscale descriptions, AI-accelerated surrogate models, and rigorous uncertainty quantification can facilitate effective scalable and intelligent application of hydrogen-based shaft furnace simulations. Full article

Chlorophyll-a (Chl-a) concentration is a key indicator of coastal ecosystem health, reflecting both primary productivity and the ecosystem’s response to climate change and human activities. This study quantifies long-term Chl-a trends in the Yellow and Bohai Seas using a multi-source remote sensing reconstruction dataset generated with deep learning algorithms. Quantile regression was applied to assess changes across the 75th, 50th, and 25th percentiles, and environmental drivers—including sea surface temperature, mixed layer depth, wind speed, and sea surface height anomalies—were evaluated in representative regions such as estuaries, aquaculture zones, and offshore waters. From 2005 to 2024, Chl-a concentrations declined across the 75th, 50th, and 25th percentiles, with rates of −4.82 × 10⁻³, −4.50 × 10⁻³, and −4.09 × 10⁻³ mg·m⁻³·a⁻¹, respectively (where “a” denotes year). The decline also showed strong seasonal differences, with summer decreases (−0.0638 mg·m⁻³·a⁻¹) substantially greater than winter (−0.04 mg·m⁻³·a⁻¹). Spatially, the decline was more pronounced in high-concentration nearshore waters, with rates of −0.0283 mg·m⁻³·a⁻¹ in the Qinhuangdao region, compared to −0.0137 mg·m⁻³·a⁻¹ in deeper offshore waters. Mixed-layer depth and wind speed emerged as the primary physical controls, with nearshore declines driven by enhanced vertical mixing and offshore changes dominated by mesoscale oceanic processes. These findings provide new insights for modeling and managing coastal ecosystems under combined climate and anthropogenic pressures. Full article